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Many studies about classification and the functional annotation of intrinsically disordered proteins (IDPs) are based on either the occurrence of long disordered regions or the fraction of disordered residues in the sequence. Taking into account both criteria we separate the human proteome, taken as a case study, into three variants of proteins: i) ordered proteins (ORDPs), ii) structured proteins with intrinsically disordered regions (IDPRs), and iii) intrinsically disordered proteins (IDPs). The focus of this work is on the different functional roles of IDPs and IDPRs, which up until now have been generally considered as a whole. Previous studies assigned a large set of functional roles to the general category of IDPs. We show here that IDPs and IDPRs have non-overlapping functional spectra, play different roles in human diseases, and deserve to be treated as distinct categories of proteins. IDPs enrich only a few classes, functions, and processes: nucleic acid binding proteins, chromatin binding proteins, transcription factors, and developmental processes. In contrast, IDPRs are spread over several functional protein classes and GO annotations which they partly share with ORDPs. As regards to diseases, we observe that IDPs enrich only cancer-related proteins, at variance with previous results reporting that IDPs are widespread also in cardiovascular and neurodegenerative pathologies. Overall, the operational separation of IDPRs from IDPs is relevant towards correct estimates of the occurrence of intrinsically disordered proteins in genome-wide studies and in the understanding of the functional spectra associated to different flavors of protein disorder.

Ecological interactions are fundamental at the cellular scale, addressing the possibility of a description of cellular systems that uses language and principles of ecology. In this work, we use a minimal ecological approach that encompasses growth, adaptation and survival of cell populations to model cell metabolisms and competition under energetic constraints. As a proof-of-concept, we apply this general formulation to study the dynamics of the onset of a specific blood cancer—called Multiple Myeloma. We show that a minimal model describing antagonist cell populations competing for limited resources, as regulated by microenvironmental factors and internal cellular structures, reproduces patterns of Multiple Myeloma evolution, due to the uncontrolled proliferation of cancerous plasma cells within the bone marrow. The model is characterized by a class of regime shifts to more dissipative states for selectively advantaged malignant plasma cells, reflecting a breakdown of self-regulation in the bone marrow. The transition times obtained from the simulations range from years to decades consistently with clinical observations of survival times of patients. This irreversible dynamical behavior represents a possible description of the incurable nature of myelomas based on the ecological interactions between plasma cells and the microenvironment, embedded in a larger complex system. The use of ATP equivalent energy units in defining stocks and flows is a key to constructing an ecological model which reproduces the onset of myelomas as transitions between states of a system which reflects the energetics of plasma cells. This work provides a basis to construct more complex models representing myelomas, which can be compared with model ecosystems.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which first occurred in Wuhan (China) in December of 2019, causes a severe acute respiratory illness with a high mortality rate, and has spread around the world. To gain an understanding of the evolution of the newly emerging SARS-CoV-2, we herein analyzed the codon usage pattern of SARS-CoV-2. For this purpose, we compared the codon usage of SARS-CoV-2 with that of other viruses belonging to the subfamily of Orthocoronavirinae. We found that SARS-CoV-2 has a high AU content that strongly influences its codon usage, which appears to be better adapted to the human host. We also studied the evolutionary pressures that influence the codon usage of five conserved coronavirus genes encoding the viral replicase, spike, envelope, membrane and nucleocapsid proteins. We found different patterns of both mutational bias and natural selection that affect the codon usage of these genes. Moreover, we show here that the two integral membrane proteins (matrix and envelope) tend to evolve slowly by accumulating nucleotide mutations on their corresponding genes. Conversely, genes encoding nucleocapsid (N), viral replicase and spike proteins (S), although they are regarded as are important targets for the development of vaccines and antiviral drugs, tend to evolve faster in comparison to the two genes mentioned above. Overall, our results suggest that the higher divergence observed for the latter three genes could represent a significant barrier in the development of antiviral therapeutics against SARS-CoV-2.

In this study, we analyze the role of different structural variants of proteins in the speciation processes. We separate human and mouse proteomes (taken as a reference) into three previously defined variants of disorder: ordered proteins (ORDPs), structured proteins with intrinsically disordered protein regions (IDPRs), and intrinsically disordered proteins (IDPs). Then, using the representation we call here Forsdyke plot, we study the correlation of DNA divergence with the corresponding protein (phenotypic) divergence in the three variants, comparing human and mouse coding sequences with their homologs from 26 eukaryotes. The parameters of the correlation are related to the speciation process. We find that the three variants of disordered proteins are differently related to the speciation process. Specifically, IDPs phenotypically diverge earlier than ORDPs and IDPRs. ORDPs diverge later but are phenotypically more reactive to nucleotide mutations than IDPRs and IDPs. Finally, IDPRs appear to diverge phenotypically later than IDPs, like ORDPs, but they are prone to accept mutations with rates that are similar to those of IDPs. We conclude that IDPs are involved in the early stages of the speciation process, whereas mutations in ORDPs, once speciation is initiated, accelerate phenotypic divergence.

The entropy production in stochastic dynamical systems is linked to the structure of their causal representation in terms of Bayesian networks. Such a connection was formalized for bipartite (or multipartite) systems with an integral fluctuation theorem in [Phys. Rev. Lett. 111, 180603 (2013)]. Here we introduce the information thermodynamics for time series, that are non-bipartite in general, and we show that the link between irreversibility and information can only result from an incomplete causal representation. In particular, we consider a backward transfer entropy lower bound to the conditional time series irreversibility that is induced by the absence of feedback in signal-response models. We study such a relation in a linear signal-response model providing analytical solutions, and in a nonlinear biological model of receptor-ligand systems where the time series irreversibility measures the signaling efficiency.

Synonymous codons, i.e., DNA nucleotide triplets coding for the same amino acid, are used differently across the variety of living organisms. The biological meaning of this phenomenon, known as codon usage bias, is still controversial. In order to shed light on this point, we propose a new codon bias index, CompAI, that is based on the competition between cognate and near-cognate tRNAs during translation, without being tuned to the usage bias of highly expressed genes. We perform a genome-wide evaluation of codon bias for E.coli, comparing CompAI with other widely used indices: tAI, CAI, and Nc. We show that CompAI and tAI capture similar information by being positively correlated with gene conservation, measured by the Evolutionary Retention Index (ERI), and essentiality, whereas, CAI and Nc appear to be less sensitive to evolutionary-functional parameters. Notably, the rate of variation of tAI and CompAI with ERI allows to obtain sets of genes that consistently belong to specific clusters of orthologous genes (COGs). We also investigate the correlation of codon bias at the genomic level with the network features of protein-protein interactions in E.coli. We find that the most densely connected communities of the network share a similar level of codon bias (as measured by CompAI and tAI). Conversely, a small difference in codon bias between two genes is, statistically, a prerequisite for the corresponding proteins to interact. Importantly, among all codon bias indices, CompAI turns out to have the most coherent distribution over the communities of the interactome, pointing to the significance of competition among cognate and near-cognate tRNAs for explaining codon usage adaptation. Notably, CompAI may potentially correlate with translation speed measurements, by accounting for the specific delay induced by wobble-pairing between codons and anticodons.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first occurred in Wuhan (China) in December of 2019. Since the outbreak, it has accumulated mutations on its coding sequences to optimize its adaptation to the human host. The identification of its genetic variants has become crucial in tracking and evaluating their spread across the globe. Methods: In this study, we compared 320,338 SARS-CoV-2 genomes isolated from all over the world to the first sequenced genome in Wuhan, China. To this end, we analysed over time the codon usage patterns of SARS-CoV-2 genes encoding for the membrane protein (M), envelope (E), spike surface glycoprotein (S), nucleoprotein (N), RNA-dependent RNA polymerase (RdRp) and ORF1ab. Results: We found that genes coding for the proteins N and S diverged more rapidly since the outbreak by accumulating mutations. Interestingly, all genes show a deoptimization of their codon usage with respect to the human host. Our findings suggest a general evolutionary trend of SARS-CoV-2, which evolves towards a sub-optimal codon usage bias to favour the host survival and its spread. Furthermore, we found that S protein and RdRp are more subject to an increasing purifying pressure over time, which implies that these proteins will reach a lower tendency to accept mutations. In contrast, proteins N and M tend to evolve more under the action of mutational bias, thus exploring a large region of their sequence space. Conclusions: Overall, our study shed more light on the evolution of SARS-CoV-2 genes and their adaptation to humans, helping to foresee their mutation patterns and the emergence of new variants.

In each genome, synonymous codons are used with different frequencies; this general phenomenon is known as codon usage bias. It has been previously recognised that codon usage bias could affect the cellular fitness and might be associated with the ecology of microbial organisms. In this exploratory study, we investigated the relationship between codon usage bias, lifestyles (thermophiles vs. mesophiles; pathogenic vs. non-pathogenic; halophilic vs. non-halophilic; aerobic vs. anaerobic and facultative) and habitats (aquatic, terrestrial, host-associated, specialised, multiple) of 615 microbial organisms (544 bacteria and 71 archaea). Principal component analysis revealed that species with given phenotypic traits and living in similar environmental conditions have similar codon preferences, as represented by the relative synonymous codon usage (RSCU) index, and similar spectra of tRNA availability, as gauged by the tRNA gene copy number (tGCN). Moreover, by measuring the average tRNA adaptation index (tAI) for each genome, an index that can be associated with translational efficiency, we observed that organisms able to live in multiple habitats, including facultative organisms, mesophiles and pathogenic bacteria, are characterised by a reduced translational efficiency, consistently with their need to adapt to different environments. Our results show that synonymous codon choices might be under strong translational selection, which modulates the choice of the codons to differently match tRNA availability, depending on the organism’s lifestyle needs. To our knowledge, this is the first large-scale study that examines the role of codon bias and translational efficiency in the adaptation of microbial organisms to the environment in which they live.

Background Natively unfolded proteins lack a well defined three dimensional structure but have important biological functions, suggesting a re-assignment of the structure-function paradigm. To assess that a given protein is natively unfolded requires laborious experimental investigations, then reliable sequence-only methods for predicting whether a sequence corresponds to a folded or to an unfolded protein are of interest in fundamental and applicative studies. Many proteins have amino acidic compositions compatible both with the folded and unfolded status, and belong to a twilight zone between order and disorder. This makes difficult a dichotomic classification of protein sequences into folded and natively unfolded ones. In this work we propose an operational method to identify proteins belonging to the twilight zone by combining into a consensus score good performing single predictors of folding. Results In this methodological paper dichotomic folding indexes are considered: hydrophobicity-charge, mean packing, mean pairwise energy, Poodle-W and a new global index, that is called here gVSL2 , based on the local disorder predictor VSL2. The performance of these indexes is evaluated on different datasets, in particular on a new dataset composed by 2369 folded and 81 natively unfolded proteins. Poodle-W, gVSL2 and mean pairwise energy have good performance and stability in all the datasets considered and are combined into a strictly unanimous combination score S SU , that leaves proteins unclassified when the consensus of all combined indexes is not reached. The unclassified proteins: i) belong to an overlap region in the vector space of amino acidic compositions occupied by both folded and unfolded proteins; ii) are composed by approximately the same number of order-promoting and disorder-promoting amino acids; iii) have a mean flexibility intermediate between that of folded and that of unfolded proteins. Conclusions Our results show that proteins unclassified by S SU belong to a twilight zone. Proteins left unclassified by the consensus score S SU have physical properties intermediate between those of folded and those of natively unfolded proteins and their structural properties and evolutionary history are worth to be investigated.